Lead the design, development, and management of large-scale, fault-tolerant, and distributed systems that deliver critical services supporting the Derivatives business.

As a Sr. Manager of Software Engineering at JPMorgan Chase within the Commercial & Investment Bank's Equites & Derivative Technology Group, you will apply your deep technical expertise across all aspects of the software development lifecycle and partner continuously with stakeholders to align on strategic goals. We embrace a culture of experimentation, continuous improvement, and learning. You'll work in a collaborative, inclusive, and intellectually stimulating environment that values diverse perspectives and creative solutions in the best interests of our global customers. You will collaborate closely with platform developers, engineering and developer communities, Quantitative Research (QR), and infrastructure teams to leverage existing firm capabilities while driving the adoption and integration of new technologies

Job Responsibilities

• Lead the design and development of scalable, resilient, and high-performance distributed systems.
• Drive architectural decisions and ensure adherence to best practices and design principles.
• Mentor and guide junior engineers, fostering a culture of technical excellence and continuous learning.
• Collaborate with cross-functional teams, including QR, platform developers, and infrastructure teams, to deliver innovative solutions.
• Evaluate and integrate emerging cloud technologies, AI/ML capabilities, and open-source tools to enhance platform capabilities.
• Ensure code quality, maintainability, and alignment with SDLC and production standards.
• Contribute to technical roadmaps, strategic planning, and process improvements.
• Leverage internal systems, proprietary platforms, and open-source technologies to ensure applications are developed in accordance with SDLC best practices, meet production-quality standards, and enable rapid deployment.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
• Minimum 12+ years of hands-on programming and application development experience, with deep expertise in the Python ecosystem and associated tooling.
• Deep expertise in Python, including advanced language features (decorators, generators, context managers, metaclasses, descriptors).
• Strong experience with asyncio, threading, multiprocessing, and concurrent futures for building high-performance, concurrent applications.
• Experience with reactive programming paradigms and frameworks such as RxPY (ReactiveX for Python) for building event-driven, asynchronous, and non-blocking applications.
• Strong understanding of complex data structures, algorithms, and their application in solving real-world problems.
• Experience building distributed applications using message queues (Kafka, RabbitMQ), caching (Redis), and distributed task processing. Deep understanding of software architecture, design patterns, distributed systems, microservices, and scalability principles.
• Deep understanding of design patterns, SOLID principles, clean code, and Pythonic idioms.
• Experience with data pipelines, feature engineering, and preprocessing for AI/ML workflows.
• Understanding of how to leverage AI for automation, anomaly detection, predictive analytics, and intelligent decision-making.
• Proven experience in leading technical projects, making architectural decisions, and mentoring engineering teams.

Preferred qualifications, capabilities, and skills

• Familiarity with financial services, derivatives, or risk management systems.
• Contributions to open-source projects or technical communities.
• Experience with Infrastructure as Code (Terraform preferred) and DevOps practices.
• Strong understanding and hands-on experience with cloud platforms (AWS preferred) and emerging technologies.
• Exposure to generative AI, LLMs, and prompt engineering.